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Title: Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

Abstract

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1345111
Report Number(s):
NREL/CP-5D00-67809
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the IEEE Power and Energy Conference, 23-24 February 2017, Champaign, Illinois
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; batteries; lithium-ion; modeling; analytical models; system integration; buildings; optimization

Citation Formats

Raszmann, Emma, Baker, Kyri, Shi, Ying, and Christensen, Dane. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint. United States: N. p., 2017. Web. doi:10.1109/PECI.2017.7935755.
Raszmann, Emma, Baker, Kyri, Shi, Ying, & Christensen, Dane. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint. United States. doi:10.1109/PECI.2017.7935755.
Raszmann, Emma, Baker, Kyri, Shi, Ying, and Christensen, Dane. Wed . "Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint". United States. doi:10.1109/PECI.2017.7935755. https://www.osti.gov/servlets/purl/1345111.
@article{osti_1345111,
title = {Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint},
author = {Raszmann, Emma and Baker, Kyri and Shi, Ying and Christensen, Dane},
abstractNote = {Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.},
doi = {10.1109/PECI.2017.7935755},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Feb 22 00:00:00 EST 2017},
month = {Wed Feb 22 00:00:00 EST 2017}
}

Conference:
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  • Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less
  • Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under differentmore » levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.« less
  • NREL's Energy Storage team is exploring the effect of mechanical crush of lithium ion cells on their thermal and electrical safety. PHEV cells, fresh as well as ones aged over 8 months under different temperatures, voltage windows, and charging rates, were subjected to destructive physical analysis. Constitutive relationship and failure criteria were developed for the electrodes, separator as well as packaging material. The mechanical models capture well, the various modes of failure across different cell components. Cell level validation is being conducted by Sandia National Laboratories.